Earphone mold electrode intelligent machining system of five-axis CNC machine tool

Abstract

The invention relates to the technical field of numerical control machine tool intelligent control, and discloses an earphone mold electrode intelligent processing system of a five-axis CNC machine tool, which comprises a five-axis linkage control module configured to cooperatively move with an A / C rotating shaft through an X / Y / Z linear axis; the multi-sensor feedback module integrates a vibration sensor, a temperature sensor and an acoustic emission probe, and collects vibration, temperature and acoustic emission data in the machining process in real time; the dynamic control logic module is used for dynamically adjusting processing parameters according to data of the multi-sensor feedback module on the basis of a pre-stored adaptive parameter library and a threshold rule; and the artificial intelligence optimization module is configured to generate processing parameters through a machine learning algorithm in combination with the historical processing data and the real-time sensor data. X / Y / Z linear axes and A / C rotating axes are coordinated through the five-axis linkage control module, vibration, temperature and acoustic emission data are collected in combination with the multi-sensor feedback module, and real-time adjustment of the posture of the tool is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control of CNC machine tools, and in particular to an intelligent processing system for earphone mold electrodes of a five-axis CNC machine tool. Background Art

[0002] Earphone mold electrodes are core components used for injection molding or die-casting in the consumer electronics and medical hearing aid industries, requiring high precision and complex geometric shapes. In existing technologies, three-axis CNC machine tools are widely used in electrode processing. Cutting is completed by moving the tool through the X / Y / Z linear axes, which is suitable for processing simple geometric shapes. Four-axis CNC machine tools improve the ability to process complex surfaces by adding a rotary axis (such as the A-axis) and are commonly used in the mold manufacturing field.

[0003] However, existing three-axis and four-axis CNC machining technologies have significant drawbacks when processing complex geometric shapes of headphone mold electrodes. Three-axis machining requires multiple clamping of the workpiece to complete deep cavity or curved surface machining. Each clamping introduces cumulative errors (up to 0.1mm), resulting in insufficient machining accuracy. Although four-axis machining can reduce the number of clamping times, it is limited to the single direction of the rotating axis and it is difficult to effectively clear corners. Deep cavity parts often have residual allowances (>0.05mm) and require subsequent manual trimming. These problems increase machining time and manual intervention, making it difficult to meet the high-precision (tolerance ≤ 0.01mm) and complex geometric shape headphone mold electrode machining requirements. Summary of the Invention

[0004] In response to the shortcomings of the existing technology, the present invention provides an intelligent processing system for headphone mold electrodes using a five-axis CNC machine tool, which solves the problem that the existing three-axis and four-axis CNC processing technologies are difficult to meet the processing requirements of headphone mold electrodes with complex geometric shapes.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent processing system for earphone mold electrodes of a five-axis CNC machine tool, comprising: A five-axis linkage control module is configured to adjust the tool posture in real time to process complex geometry earphone mold electrodes through the coordinated movement of the X / Y / Z linear axes and the A / C rotary axes; Multi-sensor feedback module, integrating vibration sensor, temperature sensor and acoustic emission probe, collects vibration, temperature and acoustic emission data in real time during the processing; a dynamic control logic module, which dynamically adjusts machining parameters, including feed rate, cutting depth, and coolant flow, according to data from the multi-sensor feedback module based on a pre-stored adaptive parameter library and threshold rules; an artificial intelligence optimization module configured to generate machining parameters using a machine learning algorithm that combines historical machining data with real-time sensor data; a personalized machining path generation module configured to generate a customized tool path based on the 3D scanning data to machine the personalized earphone mold electrode; The five-axis linkage control module works in conjunction with the multi-sensor feedback module to adjust the processing parameters through the dynamic control logic module.

[0006] Through the above technical solution, the five-axis linkage control module uses an embedded industrial PC to run a real-time operating system, coordinates the X / Y / Z linear axes and A / C rotary axes through the EtherCAT protocol, uses high-resolution encoders, and tool posture adjustment is based on the inverse kinematics algorithm. The workpiece geometry model is input and the motion instructions of each axis are output. The multi-sensor feedback module includes a sensor unit installed on the spindle housing and fixture. The data is transmitted via the CAN bus and stored in the SSD. The dynamic control logic module runs a C++ control program, including an SQLite parameter database and a PID controller. The artificial intelligence optimization module uses GPU acceleration to run a multi-layer neural network. The training data is processing records. The personalized processing path generation module is connected to a laser scanner, receives ear canal geometry data, and uses NURBS surface fitting to generate a G-code path. The five-axis linkage control module coordinates the X / Y / Z linear axes and the A / C rotary axes, and combines the multi-sensor feedback module to collect vibration, temperature and acoustic emission data to achieve real-time adjustment of the tool posture.

[0007] Preferably, the multi-sensor feedback module includes: A vibration sensor with a frequency range of 10 Hz to 10 kHz, configured to detect tool chatter and wear, with a threshold set at 40 to 60 μm; Temperature sensor with a measurement range of 0-200°C and an accuracy of ±0.3-0.7°C, configured to monitor spindle and workpiece temperatures; An acoustic emission probe with a frequency range of 20kHz-1MHz is configured to detect processing defects and material cracks; wherein the multi-sensor feedback module collects data at a frequency of 80-120Hz and transmits the data to the dynamic control logic module.

[0008] Preferably, the dynamic control logic module includes: Adaptive parameter library, storing 600-800 sets of material processing parameters, covering graphite, copper-tungsten alloy and stainless steel, including feed rate, cutting depth and spindle speed; Threshold rules, configured to reduce the feed rate by 15-25% when the vibration amplitude exceeds 40-60 μm, and trigger coolant injection at a flow rate of 0.4-0.6 L / min when the workpiece temperature exceeds 75-85°C; The tool posture optimization algorithm is based on homogeneous coordinate transformation and compensates the tool vector in real time. The deflection angle range is ±30° and the compensation accuracy is 0.0008-0.0012mm.

[0009] Preferably, the artificial intelligence optimization module includes: A neural network model, trained on 8,000-12,000 historical machining data runs, is configured to generate feed rate, depth of cut, and spindle speed; The real-time optimization function combines the data from the multi-sensor feedback module to adjust the processing parameters to adapt to material batch differences and complex working conditions.

[0010] Preferably, the personalized processing path generation module includes: 3D scanning interface, receiving ear canal geometry data with a resolution of 0.008-0.012mm; The path planning algorithm generates customized tool paths based on NURBS surface fitting; wherein, the personalized processing path generation module supports the processing of customized headphone molds.

[0011] Preferably, it further includes a closed-loop feedback module configured as follows: Recording performance data during machining, including machining time, surface roughness, and tool wear; The performance data is transmitted to the mold design system to adjust the geometry and material selection of the subsequent mold.

[0012] Preferably, the multi-sensor feedback module further comprises an optical sensor with a resolution of 0.08-0.12 μm, configured to measure surface roughness and geometric errors in real time and trigger a rework operation when non-conformity is detected.

[0013] Preferably, the dynamic control logic module further comprises a fuzzy logic control unit configured to handle nonlinear conditions during the machining process.

[0014] Preferably, an energy efficiency optimization module is also included, which is configured to control energy consumption at 7-9 kWh / piece and reduce waste generation by 15-25% by adjusting the tool path and spindle speed.

[0015] Preferably, the five-axis linkage control module further includes: A motion control unit configured to coordinate the motion trajectories of the X / Y / Z linear axes and the A / C rotary axes; Real-time calibration function, configured to calibrate inter-axis error through sensor data, with a calibration accuracy of 0.004-0.006mm.

[0016] The present invention provides an intelligent processing system for earphone mold electrodes using a five-axis CNC machine tool. It has the following beneficial effects: 1. The present invention uses a five-axis linkage control module to coordinate the X / Y / Z linear axes and the A / C rotary axes, and combines it with a multi-sensor feedback module to collect vibration, temperature, and acoustic emission data to achieve real-time adjustment of the tool posture. Compared with the existing technology of three-axis processing requiring multiple clampings and four-axis processing that cannot effectively clear corners, this system supports single clamping processing of headphone mold electrodes with complex geometric shapes.

[0017] 2. The present invention generates processing parameters through an artificial intelligence optimization module using a neural network model, combined with sensor data and historical processing data. Compared with the existing technology that relies on manual adjustment of parameters, this system adapts to material batch differences and complex working conditions through machine learning algorithms, reducing manual intervention.

[0018] 3. The present invention receives ear canal geometry data through a 3D scanning interface via a personalized processing path generation module, and uses a NURBS surface fitting algorithm to generate a customized tool path. Compared with the existing technology where standardized mold processing cannot meet personalized needs, this system supports the processing of customized earphone molds.

[0019] 4. The present invention records processing performance data through a closed-loop feedback module and transmits it to the mold design system through a RESTful API to adjust the mold geometry and material selection. Compared with the separation of design and processing in the existing technology, this system forms a design-manufacturing closed loop through data feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is the architecture diagram of the headphone mold electrode intelligent processing system of the five-axis CNC machine tool of the present invention; Figure 2 This is a working diagram of the five-axis linkage control module of the present invention; Figure 3 This is a flow chart of the dynamic control logic module processing of the present invention; Figure 4 This is a workflow diagram of the artificial intelligence optimization module of the present invention. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solution of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] Please see the attached Figure 1 -Attached Figure 4 The embodiment of the present invention provides an intelligent processing system for earphone mold electrodes using a five-axis CNC machine tool, comprising: A five-axis linkage control module is configured to adjust the tool posture in real time to process complex geometry earphone mold electrodes through the coordinated movement of the X / Y / Z linear axes and the A / C rotary axes; Multi-sensor feedback module, integrating vibration sensor, temperature sensor and acoustic emission probe, collects vibration, temperature and acoustic emission data in real time during the processing; Dynamic control logic module, based on pre-stored adaptive parameter library and threshold rules, dynamically adjusts machining parameters including feed rate, cutting depth and coolant flow according to data from multi-sensor feedback module; an artificial intelligence optimization module configured to generate machining parameters using a machine learning algorithm that combines historical machining data with real-time sensor data; a personalized machining path generation module configured to generate a customized tool path based on the 3D scanning data to machine the personalized earphone mold electrode; Among them, the five-axis linkage control module works together with the multi-sensor feedback module to adjust the processing parameters through the dynamic control logic module.

[0023] Specifically, the system's five-axis linkage control module utilizes an embedded industrial PC (processor frequency 2.5-3.5GHz, memory 16-32GB) running motion control software based on a real-time operating system (such as RTX or LinuxCNC). The software coordinates the motion of the X / Y / Z linear axes and the A / C rotary axes via the EtherCAT protocol. The X / Y / Z axes are driven by ball screws with travel ranges of 400-600mm, 300-500mm, and 200-400mm, respectively. The A-axis has a rotation range of ±110-130°, and the C-axis has a range of 360°. Both axes are equipped with high-resolution encoders (resolution 0.0005-0.001°). Tool pose adjustment is calculated using an inverse kinematics algorithm. The algorithm input is a workpiece geometry model (STL format, resolution 0.008-0.012mm) and outputs motion commands for each axis, with an update frequency of 50-100Hz.

[0024] The multi-sensor feedback module consists of three to five sensor units mounted on the spindle housing and workpiece fixture. Data is transmitted to the control unit via the CAN bus. The sensor units have an adjustable sampling rate between 80 and 120 Hz, and data is stored on a 1-2 TB SSD for real-time processing and historical analysis. The dynamic control logic module runs on the same industrial PC and utilizes a control program written in C++. This includes a parameter database (in SQLite format, storing 600-800 parameter sets) and a rules engine. Parameter adjustment is achieved via a PID controller with a response time of 10-20 ms.

[0025] The AI ​​optimization module is deployed on a GPU accelerator (NVIDIA Jetson or equivalent, with 4-8GB of video memory), running a neural network based on the TensorFlow framework. The network structure consists of 3-5 fully connected layers, using the ReLU activation function and the Adam optimizer (learning rate 0.0001-0.001). The training data consists of 8,000-12,000 machining records covering machining parameters for materials such as graphite and copper-tungsten alloy. Data preprocessing includes normalization and outlier removal (3σ criterion).

[0026] The customized machining path generation module connects to a 3D scanning device (laser scanner, wavelength 400-700nm). After receiving the ear canal geometry data, it uses a NURBS surface fitting algorithm (50-100 control points) to generate a tool path. The path file format is G-code, compatible with FANUC or Siemens CNC systems. The module runs on standalone path planning software (Python-based, requiring 2-4GB of memory), and path generation takes 1-3 minutes.

[0027] The multi-sensor feedback module includes: A vibration sensor with a frequency range of 10 Hz to 10 kHz, configured to detect tool chatter and wear, with a threshold set at 40 to 60 μm; Temperature sensor with a measurement range of 0-200°C and an accuracy of ±0.3-0.7°C, configured to monitor spindle and workpiece temperatures; The acoustic emission probe, with a frequency range of 20kHz-1MHz, is configured to detect machining defects and material cracks; wherein, the multi-sensor feedback module collects data at a frequency of 80-120Hz and transmits the data to the dynamic control logic module.

[0028] Specifically, the vibration sensor uses a piezoelectric accelerometer, mounted on the spindle housing 10-20 mm from the tool. It is secured with M4 bolts or a magnetic mount. It has a sensitivity of 50-100 mV / g and a measurement range of ±50-100 g. The sensor is connected to a data acquisition card (16-bit ADC, sampling rate 10-20 kS / s) via a shielded cable. The data is converted to a frequency domain signal using a Fourier transform (FFT window size 1024-2048) to detect tool chatter (frequency 100-500 Hz) or wear (frequency 1-5 kHz).

[0029] The temperature sensor is an infrared probe mounted on the machine table at a distance of 20-50 mm from the workpiece surface. It has an adjustable emissivity (0.9-1.0, suitable for graphite or metal) and is equipped with an automatic calibration function (calibration cycle of 1-2 weeks). The sensor transmits data via an RS485 interface. The data packet contains the temperature value and a timestamp and is stored in a ring buffer (10-20 MB) on the control unit.

[0030] The acoustic emission probe is a broadband ultrasonic sensor mounted on the bottom of the fixture. The contact surface is coated with a coupling agent (silicone grease, 0.1-0.2 mm thick). It has a sensitivity of 100-200 mV / Pa and is equipped with a preamplifier (gain 20-40 dB). The acoustic emission signal is processed through a high-pass filter (cutoff frequency 15-25 kHz) to extract characteristic values ​​(peak intensity, energy integration) for detecting cracks (signal intensity > 2-3σ) or machining defects.

[0031] The dynamic control logic module includes: Adaptive parameter library, storing 600-800 sets of material processing parameters, covering graphite, copper-tungsten alloy and stainless steel, including feed rate, cutting depth and spindle speed; Threshold rules, configured to reduce the feed rate by 15-25% when the vibration amplitude exceeds 40-60 μm, and trigger coolant injection at a flow rate of 0.4-0.6 L / min when the workpiece temperature exceeds 75-85°C; The tool posture optimization algorithm is based on homogeneous coordinate transformation and compensates the tool vector in real time. The deflection angle range is ±30° and the compensation accuracy is 0.0008-0.0012mm.

[0032] Specifically, the adaptive parameter library is stored in a SQLite database and contains 600-800 parameter sets. Each parameter set includes material type (graphite, copper-tungsten alloy, etc.), tool type (ball-end milling cutter, flat-bottom milling cutter), machining stage (roughing, finishing), and parameter values ​​(feed rate 500-1000 mm / min, depth of cut 0.1-0.5 mm, spindle speed 30,000-60,000 rpm). The database is searched using an index (material hardness HV 200-400), with a query time of less than 50 milliseconds. Parameter updates are completed via the user interface (touch screen, resolution 1920×1080) or API (RESTful, JSON format).

[0033] Threshold rules are executed by a rules engine (based on Drools). Rules are stored in XML files (1-2MB in size). Each rule contains a condition and an action. For example, a vibration rule might include: If the amplitude is 40-60μm, invoke a PID controller to reduce the feed rate by 15-25% (with a 5-10ms adjustment time); a temperature rule might include: If the workpiece temperature is 75-85°C, activate the coolant pump (100-200W power) at a flow rate of 0.4-0.6L / min and adjust the nozzle angle to ±40-50°.

[0034] The tool pose optimization algorithm, based on homogeneous coordinate transformation, runs in the control unit's real-time thread (high priority, 2-5ms cycle). The algorithm inputs are the tool position (3D coordinates, 0.001mm accuracy) and the workpiece surface normal vector, and outputs the A / C axis rotation angle (range ±30°). The calculation process includes matrix multiplication (4×4 matrix, floating-point operations) and error compensation (Newton iteration method, 3-5 iterations), with a compensation accuracy of 0.0008-0.0012mm.

[0035] The AI ​​optimization module includes: A neural network model, trained on 8,000-12,000 historical machining data runs, is configured to generate feed rate, depth of cut, and spindle speed; Real-time optimization capabilities, combined with data from multi-sensor feedback modules, adjust processing parameters to accommodate material batch differences and complex working conditions.

[0036] Specifically, the neural network model uses a multilayer perceptron (MLP) with 3-5 layers, each containing 50-100 neurons. Input features include sensor data (vibration amplitude, temperature, acoustic emission intensity), material hardness (HV 200-400), and machining stage, and outputs machining parameters (feed rate, depth of cut, spindle speed). Training data is extracted from 8,000-12,000 machining records and stored in CSV files (50-100MB in size). Preprocessing includes normalization (mean 0, variance 1) and dimensionality reduction (PCA, retaining 95% variance). Training is run on a GPU (batch size 32-64, 100-200 epochs), using the mean squared error loss function and the Adam optimizer (learning rate 0.0001-0.001). After the model is deployed, inference time is 10-20ms.

[0037] Real-time optimization is achieved through an online learning module that updates model weights every 10-20 machining cycles. This update data includes real-time sensor inputs (vibration 40-60 μm, temperature 60-80°C). The optimization algorithm interacts with the dynamic control logic module, passing parameters through shared memory (1-2 MB), with an adjustment frequency of 50-100 Hz.

[0038] The personalized machining path generation module includes: 3D scanning interface, receiving ear canal geometry data with a resolution of 0.008-0.012mm; The path planning algorithm generates customized tool paths based on NURBS surface fitting; among them, the personalized processing path generation module supports the processing of customized headphone molds.

[0039] Specifically, the 3D scanning interface supports laser scanners (wavelength 400-700nm, power 10-20mW) and receives ear canal geometry data (point cloud density 10,000-20,000 points / cm², resolution 0.008-0.012mm) via USB 3.0 or Ethernet (Gigabit speed). Data processing software (based on C++, requiring 1-2GB of memory) converts the point cloud into an STL model, with a processing time of 30-60 seconds. The model undergoes mesh optimization (50,000-100,000 triangles) and noise removal (filter radius 0.01-0.02mm).

[0040] The path planning algorithm is based on NURBS surface fitting, with 50-100 control points, degree 3-5, and a fitting error of 0.005-0.01mm. The algorithm inputs an STL model and tool parameters (diameter 0.8-1.2mm). The output is G-code (10,000-20,000 lines) containing tool paths (step size 0.01-0.02mm) and feed rates (500-1000mm / min). The path generation software runs in a separate thread (CPU utilization 10-20%) and supports multi-core parallel computing (4-8 cores).

[0041] Also included is a closed-loop feedback module configured as: Recording performance data during machining, including machining time, surface roughness, and tool wear; Transfer performance data to the mold design system to adjust the geometry and material selection of subsequent molds.

[0042] Specifically, the closed-loop feedback module runs as a background process on the control unit (occupying 100-200MB of memory) and records performance data in a SQLite database, including machining time (accurate to 0.1 second), surface roughness (measuring range Ra 0.3-0.5μm), and tool wear (wear depth 0.005-0.01mm). Data is collected at a frequency of 10-20Hz, stored for 1-2 hours, and the database size grows by 50-100MB per day.

[0043] Data is transmitted via a RESTful API (JSON format, 1-2 MB / s) over gigabit Ethernet to the mold design system (running CAD software such as CATIA or SolidWorks). Before transmission, the data is encrypted (AES-256 algorithm) and compressed (gzip, 2:1 compression ratio). The design system then adjusts the mold geometry (curvature radius 5-10 mm) or material (hardness HV 200-400) based on the data.

[0044] The multi-sensor feedback module also includes optical sensors with a resolution of 0.08-0.12μm, configured to measure surface roughness and geometric errors in real time and trigger rework operations when non-conformities are detected.

[0045] Specifically, the optical sensor uses a laser profiler, mounted on the machine tool worktable at a distance of 10-30 mm from the workpiece. It has a resolution of 0.08-0.12 μm, a scanning frequency of 50-100 Hz, and a measurement range of 5 × 5 mm². The sensor is equipped with a CMOS camera (resolution of 1280 × 1024, frame rate of 30-60 fps) and a laser (wavelength of 600-700 nm, power of 5-10 mW). Data processing includes image filtering (Gaussian kernel 3 × 3) and edge detection (Canny algorithm, threshold of 50-100), generating surface roughness and geometric error data with an accuracy of 0.001 mm.

[0046] The rework operation is triggered by the control unit when the roughness exceeds Ra 0.3-0.5μm or the geometric error exceeds 0.01mm. The rework program loads the finishing parameters (F=500-700mm / min, ap=0.05-0.1mm) and performs local processing (area 2×2mm²).

[0047] The dynamic control logic module also includes a fuzzy logic control unit configured to handle nonlinear conditions in the machining process.

[0048] Specifically, the fuzzy logic control unit runs in the control unit's real-time thread (with a cycle time of 5-10ms and a memory footprint of 50-100MB). It uses the Mamdani fuzzy inference system. Its input variables are vibration amplitude (40-60μm), temperature (75-85°C), and acoustic emission intensity (1-3σ). Its outputs are feed rate adjustment rates (-25% to +25%) and depth of cut adjustment rates (-20% to +20%). The fuzzy rule base contains 50-100 rules, such as the rule "If vibration is high and temperature is high, then the feed rate is significantly reduced."

[0049] Fuzzification uses triangular membership functions (width 10-20%), and defuzzification uses the centroid method, with a computation time of 2-5ms. The control unit interacts with the dynamic control logic module via shared memory, logging parameter adjustments in JSON format (100-200 bytes per entry).

[0050] It also includes an energy efficiency optimization module, which is configured to control energy consumption to 7-9kWh / piece and reduce scrap generation by 15-25% by adjusting tool paths and spindle speed.

[0051] Specifically, the energy efficiency optimization module runs in the control unit's optimization thread (CPU utilization 5-10%) and uses a genetic algorithm to optimize tool paths. The population size is 50-100, the number of iterations is 100-200, and the fitness function is based on energy consumption (kWh) and waste volume (g). Path optimization includes backlash minimization (backlash distance 0.5-1mm) and cutting direction adjustment (angle ±10-20°). Spindle speed is adjusted using a PID controller within a range of 30,000-60,000 rpm, with a step size of 500-1000 rpm, keeping energy consumption within 7-9 kWh per piece.

[0052] Waste reduction is achieved through layered cutting depth (3-5 layers, 0.1-0.2mm each), with waste volume recorded in a database (10-20MB per day). Optimization results are exported as G-code, compatible with CNC systems.

[0053] The five-axis linkage control module also includes: A motion control unit configured to coordinate the motion trajectories of the X / Y / Z linear axes and the A / C rotary axes; Real-time calibration function, configured to calibrate inter-axis error through sensor data, with a calibration accuracy of 0.004-0.006mm.

[0054] Specifically, the motion control unit uses a DSP chip (1-2 GHz, 512-1024 MB of memory) running a servo control algorithm (sampling period 0.1-0.2 ms), coordinating the X / Y / Z axes (speed 10-50 mm / s) and the A / C axes (angular velocity 5-10° / s). Control commands are sent to the servo drives (1-2 kW) via an EtherCAT bus (bandwidth 100-200 Mbps). Trajectory planning uses quintic polynomial interpolation with a smoothness of 0.001-0.002 mm and a planning time of 10-20 ms.

[0055] The real-time calibration function measures interaxial error (range 0.004-0.006mm) using a laser interferometer (accuracy 0.001mm), with a calibration cycle of 1-2 hours. The calibration data is stored in the control unit (size 1-2MB), and the error model is fitted using the least squares method to update the motion parameters.

[0056] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. The intelligent processing system of earphone mold electrodes for five-axis CNC machine tools is characterized by: include: A five-axis linkage control module is configured to adjust the tool posture in real time to process complex geometry earphone mold electrodes through the coordinated movement of the X / Y / Z linear axes and the A / C rotary axes; Multi-sensor feedback module, integrating vibration sensor, temperature sensor and acoustic emission probe, collects vibration, temperature and acoustic emission data in real time during the processing; a dynamic control logic module, which dynamically adjusts machining parameters, including feed rate, cutting depth, and coolant flow, according to data from the multi-sensor feedback module based on a pre-stored adaptive parameter library and threshold rules; an artificial intelligence optimization module configured to generate machining parameters using a machine learning algorithm that combines historical machining data with real-time sensor data; a personalized machining path generation module configured to generate a customized tool path based on the 3D scanning data to machine the personalized earphone mold electrode; The five-axis linkage control module works in conjunction with the multi-sensor feedback module to adjust the processing parameters through the dynamic control logic module.

2. The intelligent processing system for earphone mold electrodes of a five-axis CNC machine tool according to claim 1 is characterized in that: The multi-sensor feedback module includes: A vibration sensor with a frequency range of 10 Hz to 10 kHz, configured to detect tool chatter and wear, with a threshold set at 40 to 60 μm; Temperature sensor with a measurement range of 0-200°C and an accuracy of ±0.3-0.7°C, configured to monitor spindle and workpiece temperatures; An acoustic emission probe with a frequency range of 20kHz-1MHz is configured to detect processing defects and material cracks; wherein the multi-sensor feedback module collects data at a frequency of 80-120Hz and transmits the data to the dynamic control logic module.

3. The intelligent processing system for earphone mold electrodes of a five-axis CNC machine tool according to claim 1 is characterized in that: The dynamic control logic module includes: Adaptive parameter library, storing 600-800 sets of material processing parameters, covering graphite, copper-tungsten alloy and stainless steel, including feed rate, cutting depth and spindle speed; Threshold rules, configured to reduce the feed rate by 15-25% when the vibration amplitude exceeds 40-60 μm, and trigger coolant injection at a flow rate of 0.4-0.6 L / min when the workpiece temperature exceeds 75-85°C; The tool posture optimization algorithm is based on homogeneous coordinate transformation and compensates the tool vector in real time. The deflection angle range is ±30° and the compensation accuracy is 0.0008-0.0012mm.

4. The intelligent processing system for earphone mold electrodes of a five-axis CNC machine tool according to claim 1 is characterized in that: The artificial intelligence optimization module includes: A neural network model, trained on 8,000-12,000 historical machining data runs, is configured to generate feed rate, depth of cut, and spindle speed; The real-time optimization function combines the data from the multi-sensor feedback module to adjust the processing parameters to adapt to material batch differences and complex working conditions.

5. The intelligent processing system for earphone mold electrodes of a five-axis CNC machine tool according to claim 1 is characterized in that: The personalized processing path generation module includes: 3D scanning interface, receiving ear canal geometry data with a resolution of 0.008-0.012mm; The path planning algorithm generates customized tool paths based on NURBS surface fitting; wherein, the personalized processing path generation module supports the processing of customized headphone molds.

6. The intelligent processing system for earphone mold electrodes of a five-axis CNC machine tool according to claim 1 is characterized in that: Also included is a closed-loop feedback module configured as: Recording performance data during machining, including machining time, surface roughness, and tool wear; The performance data is transmitted to the mold design system to adjust the geometry and material selection of the subsequent mold.

7. The intelligent processing system for earphone mold electrodes of a five-axis CNC machine tool according to claim 1 is characterized in that: The multi-sensor feedback module also includes an optical sensor with a resolution of 0.08-0.12μm, which is configured to measure surface roughness and geometric errors in real time and trigger rework operations when non-conformities are detected.

8. The intelligent processing system for earphone mold electrodes of a five-axis CNC machine tool according to claim 1 is characterized in that: The dynamic control logic module also includes a fuzzy logic control unit configured to handle nonlinear conditions in the machining process.

9. The intelligent processing system for earphone mold electrodes of a five-axis CNC machine tool according to claim 1 is characterized in that: It also includes an energy efficiency optimization module, which is configured to control energy consumption to 7-9kWh / piece and reduce scrap generation by 15-25% by adjusting tool paths and spindle speed.

10. The intelligent processing system for earphone mold electrodes of a five-axis CNC machine tool according to claim 1 is characterized in that: The five-axis linkage control module also includes: A motion control unit configured to coordinate the motion trajectories of the X / Y / Z linear axes and the A / C rotary axes; Real-time calibration function, configured to calibrate inter-axis error through sensor data, with a calibration accuracy of 0.004-0.006mm.

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